公开(公告)号：US20170287675A1

公开(公告)日：2017-10-05

申请号：US15387388

申请日：2016-12-21

Applicant: KLA-Tencor Corporation

Inventor： Arjun Hegde ,  Luca Grella ,  Christopher Sears

IPC: H01J37/153 ,  H01J37/244 ,  H01J37/18 ,  H01J37/26 ,  H01J37/10 ,  H01J37/20 ,  H01J37/28

CPC classification number: H01J37/153 ,  H01J37/10 ,  H01J37/18 ,  H01J37/20 ,  H01J37/244 ,  H01J37/265 ,  H01J37/28 ,  H01J2237/0044 ,  H01J2237/103 ,  H01J2237/20285 ,  H01J2237/221

Abstract: A scanning electron microscopy system is disclosed. The system includes a sample stage configured to secure a sample having conducting structures disposed on an insulating substrate. The system includes an electron-optical column including an electron source configured to generate a primary electron beam and a set of electron-optical elements configured to direct at least a portion of the primary electron beam onto a portion of the sample. The system includes a detector assembly configured to detect electrons emanating from the surface of the sample. The system includes a controller communicatively coupled to the detector assembly. The controller is configured to direct the electron-optical column and stage to perform, with the primary electron beam, an alternating series of image scans and flood scans of the portion of the sample, wherein each of the flood scans are performed sequential to one or more of the imaging scans.

公开(公告)号：US10460903B2

公开(公告)日：2019-10-29

申请号：US15387388

申请日：2016-12-21

Applicant: KLA-Tencor Corporation

Inventor： Arjun Hegde ,  Luca Grella ,  Christopher Sears

IPC: H01J37/153 ,  H01J37/10 ,  H01J37/18 ,  H01J37/20 ,  H01J37/244 ,  H01J37/26 ,  H01J37/28

Abstract: A scanning electron microscopy system is disclosed. The system includes a sample stage configured to secure a sample having conducting structures disposed on an insulating substrate. The system includes an electron-optical column including an electron source configured to generate a primary electron beam and a set of electron-optical elements configured to direct at least a portion of the primary electron beam onto a portion of the sample. The system includes a detector assembly configured to detect electrons emanating from the surface of the sample. The system includes a controller communicatively coupled to the detector assembly. The controller is configured to direct the electron-optical column and stage to perform, with the primary electron beam, an alternating series of image scans and flood scans of the portion of the sample, wherein each of the flood scans are performed sequential to one or more of the imaging scans.

公开(公告)号：US20190294923A1

公开(公告)日：2019-09-26

申请号：US16357360

申请日：2019-03-19

Applicant: KLA-Tencor Corporation

Inventor： Ian Riley ,  Li He ,  Sankar Venkataraman ,  Michael Kowalski ,  Arjun Hegde

IPC: G06K9/62 ,  G06N20/00 ,  G06F3/0482 ,  G06T7/00

Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.

公开(公告)号：US11170255B2

公开(公告)日：2021-11-09

申请号：US16357360

申请日：2019-03-19

Applicant: KLA-Tencor Corporation

Inventor： Ian Riley ,  Li He ,  Sankar Venkataraman ,  Michael Kowalski ,  Arjun Hegde

IPC: G06K9/00 ,  G06K9/62 ,  G06N20/00 ,  G06F3/0482 ,  G06T7/00 ,  G06N3/08

Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.